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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Pytorch Unet Module used for diffusion.
"""
from dataclasses import dataclass
import typing as tp
import torch
from torch import nn
from torch.nn import functional as F
from audiocraft.modules.transformer import StreamingTransformer, create_sin_embedding
@dataclass
class Output:
sample: torch.Tensor
def get_model(cfg, channels: int, side: int, num_steps: int):
if cfg.model == 'unet':
return DiffusionUnet(
chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
else:
raise RuntimeError('Not Implemented')
class ResBlock(nn.Module):
def __init__(self, channels: int, kernel: int = 3, norm_groups: int = 4,
dilation: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
dropout: float = 0.):
super().__init__()
stride = 1
padding = dilation * (kernel - stride) // 2
Conv = nn.Conv1d
Drop = nn.Dropout1d
self.norm1 = nn.GroupNorm(norm_groups, channels)
self.conv1 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
self.activation1 = activation()
self.dropout1 = Drop(dropout)
self.norm2 = nn.GroupNorm(norm_groups, channels)
self.conv2 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
self.activation2 = activation()
self.dropout2 = Drop(dropout)
def forward(self, x):
h = self.dropout1(self.conv1(self.activation1(self.norm1(x))))
h = self.dropout2(self.conv2(self.activation2(self.norm2(h))))
return x + h
class DecoderLayer(nn.Module):
def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
dropout: float = 0.):
super().__init__()
padding = (kernel - stride) // 2
self.res_blocks = nn.Sequential(
*[ResBlock(chin, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
for idx in range(res_blocks)])
self.norm = nn.GroupNorm(norm_groups, chin)
ConvTr = nn.ConvTranspose1d
self.convtr = ConvTr(chin, chout, kernel, stride, padding, bias=False)
self.activation = activation()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.res_blocks(x)
x = self.norm(x)
x = self.activation(x)
x = self.convtr(x)
return x
class EncoderLayer(nn.Module):
def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
dropout: float = 0.):
super().__init__()
padding = (kernel - stride) // 2
Conv = nn.Conv1d
self.conv = Conv(chin, chout, kernel, stride, padding, bias=False)
self.norm = nn.GroupNorm(norm_groups, chout)
self.activation = activation()
self.res_blocks = nn.Sequential(
*[ResBlock(chout, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
for idx in range(res_blocks)])
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, C, T = x.shape
stride, = self.conv.stride
pad = (stride - (T % stride)) % stride
x = F.pad(x, (0, pad))
x = self.conv(x)
x = self.norm(x)
x = self.activation(x)
x = self.res_blocks(x)
return x
class BLSTM(nn.Module):
"""BiLSTM with same hidden units as input dim.
"""
def __init__(self, dim, layers=2):
super().__init__()
self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
self.linear = nn.Linear(2 * dim, dim)
def forward(self, x):
x = x.permute(2, 0, 1)
x = self.lstm(x)[0]
x = self.linear(x)
x = x.permute(1, 2, 0)
return x
class DiffusionUnet(nn.Module):
def __init__(self, chin: int = 3, hidden: int = 24, depth: int = 3, growth: float = 2.,
max_channels: int = 10_000, num_steps: int = 1000, emb_all_layers=False, cross_attention: bool = False,
bilstm: bool = False, transformer: bool = False,
codec_dim: tp.Optional[int] = None, **kwargs):
super().__init__()
self.encoders = nn.ModuleList()
self.decoders = nn.ModuleList()
self.embeddings: tp.Optional[nn.ModuleList] = None
self.embedding = nn.Embedding(num_steps, hidden)
if emb_all_layers:
self.embeddings = nn.ModuleList()
self.condition_embedding: tp.Optional[nn.Module] = None
for d in range(depth):
encoder = EncoderLayer(chin, hidden, **kwargs)
decoder = DecoderLayer(hidden, chin, **kwargs)
self.encoders.append(encoder)
self.decoders.insert(0, decoder)
if emb_all_layers and d > 0:
assert self.embeddings is not None
self.embeddings.append(nn.Embedding(num_steps, hidden))
chin = hidden
hidden = min(int(chin * growth), max_channels)
self.bilstm: tp.Optional[nn.Module]
if bilstm:
self.bilstm = BLSTM(chin)
else:
self.bilstm = None
self.use_transformer = transformer
self.cross_attention = False
if transformer:
self.cross_attention = cross_attention
self.transformer = StreamingTransformer(chin, 8, 6, bias_ff=False, bias_attn=False,
cross_attention=cross_attention)
self.use_codec = False
if codec_dim is not None:
self.conv_codec = nn.Conv1d(codec_dim, chin, 1)
self.use_codec = True
def forward(self, x: torch.Tensor, step: tp.Union[int, torch.Tensor], condition: tp.Optional[torch.Tensor] = None):
skips = []
bs = x.size(0)
z = x
view_args = [1]
if type(step) is torch.Tensor:
step_tensor = step
else:
step_tensor = torch.tensor([step], device=x.device, dtype=torch.long).expand(bs)
for idx, encoder in enumerate(self.encoders):
z = encoder(z)
if idx == 0:
z = z + self.embedding(step_tensor).view(bs, -1, *view_args).expand_as(z)
elif self.embeddings is not None:
z = z + self.embeddings[idx - 1](step_tensor).view(bs, -1, *view_args).expand_as(z)
skips.append(z)
if self.use_codec: # insert condition in the bottleneck
assert condition is not None, "Model defined for conditionnal generation"
condition_emb = self.conv_codec(condition) # reshape to the bottleneck dim
assert condition_emb.size(-1) <= 2 * z.size(-1), \
f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
if not self.cross_attention:
condition_emb = torch.nn.functional.interpolate(condition_emb, z.size(-1))
assert z.size() == condition_emb.size()
z += condition_emb
cross_attention_src = None
else:
cross_attention_src = condition_emb.permute(0, 2, 1) # B, T, C
B, T, C = cross_attention_src.shape
positions = torch.arange(T, device=x.device).view(1, -1, 1)
pos_emb = create_sin_embedding(positions, C, max_period=10_000, dtype=cross_attention_src.dtype)
cross_attention_src = cross_attention_src + pos_emb
if self.use_transformer:
z = self.transformer(z.permute(0, 2, 1), cross_attention_src=cross_attention_src).permute(0, 2, 1)
else:
if self.bilstm is None:
z = torch.zeros_like(z)
else:
z = self.bilstm(z)
for decoder in self.decoders:
s = skips.pop(-1)
z = z[:, :, :s.shape[2]]
z = z + s
z = decoder(z)
z = z[:, :, :x.shape[2]]
return Output(z)
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